A NOVEL APPROACH FOR DATA PUBLISHING IN MINING
Journal Title: International Journal of Research in Computer and Communication Technology - Year 2013, Vol 2, Issue 7
Abstract
In recent years, advances in technology have lead to an increase in the capability to store and record personal data about consumers and individuals. This has guide to concerns that the personal data may be misused for a variety of purposes. In order to improve this number of techniques has recently been proposed to perform the data mining tasks in a privacy preserving way. These techniques designed for performing privacy-preserving data mining are drawn from a wide array of related topics such as data mining. Here we present regular review for many forms of several anonymization techniques like generalization and bucketization, have been planned for private preserving micro data publishing. Our work has presented that generalization lose preferred amount of information for elevated-dimensional data. The tender, bucketization does not protect member ship confession. Where slicing is a method that preserves better data utility when compare to generalization and protects member ship disclosure better than bucketization. Here main focus on effective method that can be used s long as better data usage and it can maintain high-dimensional data.
Authors and Affiliations
Yedu kondalu, SK Mohiddin, M. Kiran Kumar
Keywords
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